Visual attention modeling is generally used to address the observed and/or predicted behavior of human and non-human visual attention such that photographic related applications (e.g., displaying an image on an electronic display) can be improved by producing images that will be more likely to capture and/or retain the attention of an observer. Such modeling may utilize visual saliency evaluation, which focuses on detecting regions in an image that are highly dissimilar from various other parts of the image. However, such modeling may fail in instances where an image does not contain highly dissimilar regions or when the highly dissimilar regions are not visually significant or aesthetically attractive.
Accordingly, a need exists for visual attention modeling that more accurately observes and/or predicts attention and improves use of the observed and/or predicted attention in presenting photographic related data to and/or storing the data for users of electronic devices.